Imperial College London
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DrAlessiaDavid

Faculty of Natural Sciences, Department of Life Sciences

Lecturer in Bioinformatics and Data Intensive Biology
 
 
 
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Contact

 

+44 (0)20 7594 5333alessia.david09

 
 
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Location

 

Department of BioinformaticsSir Ernst Chain BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ittisoponpisan:2018:10.1210/js.2018-00077,
author = {Ittisoponpisan, S and David, A},
doi = {10.1210/js.2018-00077},
journal = {Journal of the Endocrine Society},
pages = {842--854},
title = {Structural biology helps interpret variants of uncertain significance in genes causing endocrine and metabolic disorders},
url = {http://dx.doi.org/10.1210/js.2018-00077},
volume = {2},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - ContextVariants of uncertain significance (VUSs) lack sufficient evidence, in terms of statistical power or experimental studies, to allow unequivocal determination of their damaging effect. VUSs are a major burden in performing genetic analysis. Although in silico prediction tools are widely used, their specificity is low, thus urgently calling for methods for prioritizing and characterizing variants.ObjectiveTo assess the frequency of VUSs in genes causing endocrine and metabolic disorders, the concordance rate of predictions from different in silico methods, and the added value of three-dimensional protein structure analysis in discerning and prioritizing damaging variants.ResultsA total of 12,266 missense variants reported in 641 genes causing endocrine and metabolic disorders were analyzed. Among these, 4123 (33.7%) were VUSs, of which 2010 (48.8%) were predicted to be damaging and 1452 (35.2%) were predicted to be tolerated according to in silico tools. A total of 5383 (87.7%) of 6133 disease-causing variants and 823 (55.8%) of 1474 benign variants were correctly predicted. In silico predictions were noninformative in 5.7%, 14.4%, and 16% of damaging, benign, and VUSs, respectively. A damaging effect on 3D protein structure was present in 240 (30.9%) of predicted damaging and 40 (9.7%) of predicted tolerated VUSs (P < 0.001). An in-depth analysis of nine VUSs occurring in TSHR, LDLR, CASR, and APOE showed that they greatly affect protein stability and are therefore strong candidates for disease.ConclusionsIn our dataset, we confirmed the high sensitivity but low specificity of in silico predictions tools. 3D protein structural analysis is a compelling tool for characterizing and prioritizing VUSs and should be a part of genetic variant analysis.
AU  - Ittisoponpisan,S
AU  - David,A
DO  - 10.1210/js.2018-00077
EP  - 854
PY  - 2018///
SN  - 2472-1972
SP  - 842
TI  - Structural biology helps interpret variants of uncertain significance in genes causing endocrine and metabolic disorders
T2  - Journal of the Endocrine Society
UR  - http://dx.doi.org/10.1210/js.2018-00077
UR  - http://hdl.handle.net/10044/1/61252
VL  - 2
ER  -
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